scholarly journals Combinatorial Design of the MAUT and PAMSSEM II Methods for Multiple Attributes Group Decision Making with Probabilistic Linguistic Information

2022 ◽  
Author(s):  
Qiaoyu Kong ◽  
Liangping Wu
Keyword(s):  
Decision Making ◽  
Utility Function ◽  
Group Decision Making ◽  
Consumer Satisfaction ◽  
Group Decision ◽  
Entropy Method ◽  
Combinatorial Design ◽  
Variation Method ◽  
Multiple Attribute ◽  
Probabilistic Linguistic Term Sets

Abstract This paper considers the application of probabilistic linguistic term sets (PLTS) in multiple-attribute group decision-making (MAGDM) when the weights can’t be determined. First, as an improvement of the PROMETHEE method, the PAMSSEM method can not only handle missing evaluations, but also proposes a rejection threshold to calculate the overall consistency of the plan, so as to rank the plan more reasonably. At the same time, the MAUT uses the marginal utility function to reallocate the attribute values of the alternatives in the interval , and then calculate the total utility to sort them. Because the utility function is beneficial in expressing consumer satisfaction, we combine the MAUT method and PAMSSEM II method and apply it to solve decision-making problems under probabilistic linguistic environment. Secondly the coefficient of variation method, entropy method and analytic hierarchy process are used to calculate the weights in a combination. In the process of data processing, we use the transfer function to convert the PLTS into the hesitant probabilistic fuzzy set (HPFS) for calculation. Finally, the PL-MAUT-PAMSSEM II method, PROMRTHE method, TOPSIS method and ARAS method are compared with each other.

CPT-MABAC method for spherical fuzzy multiple attribute group decision making and its application to green supplier selection

2021 ◽  
pp. 1-11
Author(s):  
Huiyuan Zhang ◽  
Guiwu Wei ◽  
Xudong Chen
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Supplier Selection ◽  
Group Decision ◽  
Comparison Method ◽  
Entropy Method ◽  
Decision Makers ◽  
Green Supplier Selection ◽  
Multiple Attribute ◽  
Psychological Perceptions

The green supplier selection is one of the popular multiple attribute group decision making (MAGDM) problems. The spherical fuzzy sets (SFSs) can fully express the complexity and fuzziness of evaluation information for green supplier selection. Furthermore, the classic MABAC (multi-attributive border approximation area comparison) method based on the cumulative prospect theory (CPT-MABAC) is designed, which is an optional method in reflecting the psychological perceptions of decision makers (DMs). Therefore, in this article, we propose a spherical fuzzy CPT-MABAC (SF-CPT-MABAC) method for MAGDM issues. Meanwhile, considering the different preferences of DMs to attribute sets, we obtain the objective weights of attributes through entropy method. Focusing on the current popular problems, this paper applies the proposed method for green supplier selection and proves for green supplier selection based on SF-CPT-MABAC method. Finally, by comparing existing methods, the effectiveness of the proposed method is certified.

Model for selection of hospital constructions with probabilistic linguistic GRP method

2021 ◽  
Vol 40 (1) ◽  
pp. 1245-1259
Author(s):  
Siqi Wang ◽  
Guiwu Wei ◽  
Jiang Wu ◽  
Cun Wei ◽  
Yanfeng Guo
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Correlation Method ◽  
Ideal Solution ◽  
Multiple Attribute ◽  
Probabilistic Linguistic Term Sets ◽  
Criteria Importance ◽  
Grey Relational ◽  
Selection Of

Probabilistic linguistic term sets are used to express uncertain decision information in multiple attribute group decision making problems. For probabilistic linguistic multiple attribute group decision making (MAGDM) with weight determined by CRITIC (Criteria Importance Through Intercriteria Correlation) method, the probabilistic linguistic grey relational projection method is proposed in this paper. Firstly, the correlation coefficient among attributes and standard deviation of each attribute are utilized to compute the attributes weights. Then the most ideal alternative is decided by means of counting the grey relational projection (GRP) from probabilistic linguistic positive ideal solution and probabilistic linguistic negative ideal solution. In the end, a numerical example for site selection of hospital constructions is applied to further account for the extended method. The result demonstrates the availability of the proposed method and it can be used in other fields which refers to problems of selection.

scholarly journals Probabilistic Linguistic Aggregation Operators Based on Einstein t-Norm and t-Conorm and Their Application in Multi-Criteria Group Decision Making

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 39 ◽  
Author(s):  
Kobina Agbodah ◽  
Adjei Peter Darko
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Information Aggregation ◽  
Aggregation Operators ◽  
Weighted Averaging ◽  
Knowledge Based ◽  
Multiple Attribute ◽  
Linguistic Term ◽  
Probabilistic Linguistic Term Sets

One of the major problems of varied knowledge-based systems has to do with aggregation and fusion. Pang’s probabilistic linguistic term sets denotes aggregation of fuzzy information and it has attracted tremendous interest from researchers recently. The purpose of this article is to deal investigating methods of information aggregation under the probabilistic linguistic environment. In this situation we defined certain Einstein operational laws on probabilistic linguistic term elements (PLTESs) based on Einstein product and Einstein sum. Consequently, we develop some probabilistic linguistic aggregation operators, notably the probabilistic linguistic Einstein average (PLEA) operators, probabilistic linguistic Einstein geometric (PLEG) operators, weighted probabilistic linguistic Einstein average (WPLEA) operators, weighted probabilistic linguistic Einstein geometric (WPLEG) operators. These operators extend the weighted averaging operator and the weighted geometric operator for the purpose of aggregating probabilistic linguistic terms values respectively. Einstein t-norm and Einstein t-conorm constitute effective aggregation tools and they allow input arguments to reinforce each other downwardly and upwardly respectively. We then generate various properties of these operators. With the aid of the WPLEA and WPLEG, we originate the approaches for the application of multiple attribute group decision making (MAGDM) with the probabilistic linguistic term sets (PLTSs). Lastly, we apply an illustrative example to elucidate our proposed methods and also validate their potentials.

scholarly journals A MAGDM Algorithm with Multi-Granular Probabilistic Linguistic Information

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 127 ◽  
Author(s):  
Ju-Xiang Wang
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Entropy Method ◽  
The Other ◽  
Cross Entropy ◽  
Distance Measures ◽  
Cross Entropy Method ◽  
Linguistic Term ◽  
Probabilistic Linguistic Term Sets

The traditional multi-attribute group decision making (MAGDM) method needs to be improved to the integration of assessment information under multi-granular probabilistic linguistic environments. Some novel distance measures between two multi-granular probabilistic linguistic term sets (PLTSs) are proposed, and distance measures are proved to be reasonable. To calculate the weights of the alternative attributes, the extended cross-entropy method for multi-granular probabilistic linguistic term sets is proposed. Then, a novel extended MAGDM algorithm based on prospect theory (PT) is proposed. Two case studies of decision making (DM) on purchasing a car is provided to illustrate the application of the extended MAGDM algorithm. The case analyses are proposed to illustrate the novelty, feasibility, and application of the proposed MAGDM algorithm by comparing the other three algorithms based on TOPSIS, VIKOR, and Pang Qi et al.’s method. The analyses results demonstrate that the proposed algorithm based on PT is superior.

scholarly journals Public Transportation Hub Location with Stochastic Demand: An Improved Approach Based on Multiple Attribute Group Decision-Making

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Sen Liu ◽  
Zhilan Song ◽  
Shuqi Zhong
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Public Transportation ◽  
Group Decision ◽  
Numerical Study ◽  
Hub Location ◽  
Hub Location Problem ◽  
The Public ◽  
Multiple Attribute ◽  
Qualitative Factors

Urban public transportation hubs are the key nodes of the public transportation system. The location of such hubs is a combinatorial problem. Many factors can affect the decision-making of location, including both quantitative and qualitative factors; however, most current research focuses solely on either the quantitative or the qualitative factors. Little has been done to combine these two approaches. To fulfill this gap in the research, this paper proposes a novel approach to the public transportation hub location problem, which takes both quantitative and qualitative factors into account. In this paper, an improved multiple attribute group decision-making (MAGDM) method based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and deviation is proposed to convert the qualitative factors of each hub into quantitative evaluation values. A location model with stochastic passenger flows is then established based on the above evaluation values. Finally, stochastic programming theory is applied to solve the model and to determine the location result. A numerical study shows that this approach is applicable and effective.

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